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Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.

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<p>Over Fennoscandian mountain birch forest region, there are increased attacks of geometrid moth larvae. These herbivores can change forests from a carbon sink to a carbon source. When moths start to chew on leaves, large quantities of biogenic volatile organic compounds (BVOCs) are released. Herbivory-induced BVOC emissions have been observed and quantified at a few sites over Fennoscandian mountain birch forest, but we know very little of their potential regional implications for atmospheric processes.</p> <p>In this work, we extracted birch defoliation information based on MODIS leaf area index (LAI) for an outbreak year 2012, and together with field-observed relationship between leaf defoliated level and changes in emissions, we modelled herbivory-induced BVOC emissions at regional scale using MEGAN. Taking a step further, we fed MEGAN-modelled BVOC emission data with or without considering herbivory impacts to a two-way coupled WRF-CMAQ system to dynamically assess the impacts of these emissions on the atmospheric chemistry and climate system .</p> <p>During the whole growing season of 2012, the defoliation at some MODIS grids can be as high as 90%, and the large defoliation mainly occurs in June and July. For <em>t</em>-<em>β</em>-ocimene, Other Monoterpenes, Stress and Other compound groups, herbivory contributes to more than 30, 8, 5 and 16 times the increase in the seasonal sum for the defoliated regions. For terpenes, herbivory increased monthly emissions up to 3 times for June and July. The reduction of emissions caused by herbivory-caused decrease in LAI is much smaller than the herbivory-induced increase. We also found strong impacts of herbivory-induced BVOC emissions on downward shortwave radiation and cloud radiative forcing.</p> <p>This is the first time we can link all these components, i.e., satellite monitoring of leaf defoliation, in-situ observation, ecosystem and atmospheric modelling together to answer the research questions related to the regional importance of insect herbivory on atmospheric composition and climate.</p>